Module manager: Ruheyan Nuermaimaiti
Email: R.Nuermaimaiti@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2024/25
MATH5741M, or equivalent.
This module is not approved as an Elective
In big data with multiple variables, it is vital to discover pattern and infer valuable information from the data. This module introduces basic techniques from multivariate statistics, with the aim to discover, describe and exploit dependencies between variables in complex datasets.
To introduce basic techniques from multivariate statistics, with the aim to discover, describe and exploit dependencies between variables in complex datasets.
On completion of the module, the student should:
- be able to discover and exploit dependency between variables;
- be able to reduce the dimension of a dataset with dependent components, and interpret the results;
- be able to identify clusters in a given data set;
- be able to visualise similarities between observations in lower dimension.
- Introduction to multivariate analysis
- Statistical dependence, covariance matrix
- High dimensional problems, the "curse of dimensionality"
- Principal Component Analysis (PCA), dimension reduction
- Clustering, K-means method, distances between/within clusters
- Multidimensional Scaling (MDS)
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | Delivery type 22 | Number 1 | Length hours 22 |
| Practical | Delivery type 11 | Number 1 | Length hours 11 |
| Private study hours | Delivery type 117 | ||
| Total Contact hours | Delivery type 33 | ||
| Total hours (100hr per 10 credits) | Delivery type 150 | ||
The student will be expected to complete regular written worksheet assignments testing their understanding of theoretical course elements.
The student will learn to perform analysis using the software package R, which includes performing dimension reduction such as principal component analysis and factor analysis, clustering such as hierarchical and k-means clustering, multi-dimensional scaling, and learning different types of plots for presenting high-dimensional data into informative two-dimensional. Part of the assessment for the module consists of a practical, where the student will apply these techniques to a real-world data set.
Monitoring by regular worksheets and achievement in supervised practical sessions.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Assessment type Practical | Notes Report | % of formal assessment 30 |
| Total percentage (Assessment Coursework) | Assessment type 30 | |
There is no resit available for the coursework components of this module. If the module is failed, the coursework mark will be carried forward and added to the resit exam mark with the same weighting as listed above.
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Exam type Standard exam (closed essays, MCQs etc) | Exam duration 2.0 Hrs 0 Mins | % of formal assessment 70 |
| Total percentage (Assessment Exams) | Exam type 70 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
The reading list is available from the Library website
Last updated: 4/29/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team